The accurate detection of disease clusters is a well acknowledged problem. False detections may be expected under repetitive testing, but the opposite situation, that of failing to detect clusters when they actually exist, is increasingly recognized. Both F=false detections and failures-to-detect have striking implications for epidemiological studies, since space-time clustering is often a first step in the analysis of suspected public health problems. The failure to find existing clusters means that real disease clusters will be ignored, but false detections result in a loss of public confidence and wasted resources. Clearly, studies are required to determine the abilities of space-time clustering methods under different spatial and temporal patterns of disease incidence. This projects objectives are to (1) complete development of the user-friendly, interactive program CAST (Cluster Analysis in Space and Time) initiated in Phase I. (2) Determine the ability of existing methods to detect cancer clusters, and (3) develop novel, sensitive clustering methods. CAST serves two purposes: Cluster description through mapping and graphics and cluster detection using sophisticated statistical techniques. CAST provides the software tool needed by academe, government and private industry to investigate disease clusters. CAST will serve as the software platform in the phase II research, which will determine the sensitivity of existing cluster detection techniques and develop new cluster detection methodologies.